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Github Repositories of the Chapters Containing the Data, Code and Notebook for the Case Studies
Table of Contents
Appendixes
| Case_Study | Title | Virtual_RStudio_Server |
|---|---|---|
| 1B | Clustering of Documents using R | link |
| 4C | Topic Modeling of Documents using R | link |
| 5B | Network Text Analysis of Documents using Textnets package of R | link |
| 6B | Burst Detection of Documents using R | link |
| 7B | Sentiment Analysis of Documents using R | link |
| 9A | To Make a Dashboard using R | link |
Reproduce the analysis in the cloud without having to install any software. The computational environment used by the authors is run using BinderHub. Click the hyperlink to open an interactive virtual RStudio environment for a hands-on practice for the case studies that used R programming language.
| Case_Study | Title | Virtual_Jupyter_Notebook |
|---|---|---|
| 1B | Clustering of Documents using R | link |
| 4C | Topic Modeling of Documents using R | link |
| 5B | Network Text Analysis of Documents using Textnets package of R | link |
| 6B | Burst Detection of Documents using R | link |
| 7B | Sentiment Analysis of Documents using R | link |
| 9A | To Make a Dashboard using R | link |
Reproduce the analysis in the cloud without having to install any software. The computational environment used by the authors is run using BinderHub. Click the hyperlink to open an interactive virtual Jupyter Notebook for a hands-on practice for the case studies that used R programming language.
©2021 Lamba and Madhusdhan - all rights reserved
The heatmap plot shows the distances between the documents.
©2021 Lamba and Madhusdhan - all rights reserved
The clustered heatmap plot shows another way to visualize the distances between the documents.
©2021 Lamba and Madhusdhan - all rights reserved
The dendogram presents the hierarichal clustering of documents using the ward method.
©2021 Lamba and Madhusdhan - all rights reserved
For clustering in R, elbow method was used to determine the number of clusters.
©2021 Lamba and Madhusdhan - all rights reserved
Euclidean distance method was used to determine the distance between the documents.
©2021 Lamba and Madhusdhan - all rights reserved
Hierarchical clustering with dendrograms is another way to visualise the distance between the documents.
©2021 Lamba and Madhusdhan - all rights reserved
Circular dendogram is yet another way to visualise the distance between the documents.
©2021 Lamba and Madhusdhan - all rights reserved
Phylogenic structure is another way of visualizing the same results with different perspective according to your research problem and dataset.
Timeline showing the core topics in DESIDOC Journal of Library and Information Technology from 1981 to 2018 (©2019 Springer Nature, all rights reserved – reprinted with permission from Springer Nature, published in Lamba and Madhusudhan (2019))
50 core topics were identified that fitted the corpus of 928 DJLIT research articles wherein only 29 topics were identified as unique.
Latent Dirichlet Allocation Topic and Word Result for PQDT Global ETDs during 2014-2018 (©2020 Cadernos BAD, all rights reserved – reprinted under Creative Commons CC BY license, published in Lamba and Madhusudhan (2020) )
The results shows the topics assigned to the corpus of ETDs.
©2021 Lamba and Madhusdhan - all rights reserved
The figure shows the results for 5 topics using Structural Topic Modeling (STM).
©2021 Lamba and Madhusdhan - all rights reserved
The figure shows second way of representing the results from Method 1.
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The figure shows third way of representing the results from Method 1 and 2.
©2021 Lamba and Madhusdhan - all rights reserved
The figure shows fourth way of representing the results from Method 1, 2, and 3.
©2021 Lamba and Madhusdhan - all rights reserved
The Table presents the result for top five representative ETDs for the modeled topics and are ranked according to their probability.
©2021 Lamba and Madhusdhan - all rights reserved
The figure shows correlation between the topics using a network graph.
Word Co-Occurrence Network (©2021 Lamba and Madhusdhan - all rights reserved)
The figure presents the word co-occurrence network for top 50 words that represent the literature indexed in Web of Science (WoS) database on malaria disease for year 2019.
Text Network (©2021 Lamba and Madhusdhan - all rights reserved)
The figure represents the 22 clusters/communities of 238 words (nodes) which were determined from the network text analysis of the data.
Polarity Percentage (©2018 Springer Nature, all rights reserved – reprinted with permission from Springer Nature, published in Lamba and Madhusudhan (2018))
The figure represents the percentage comparison between polarities for 20 different productivity facets.
Subjectivity Percentage (©2018 Springer Nature, all rights reserved – reprinted with permission from Springer Nature, published in Lamba and Madhusudhan (2018))
The figure represents the percentage comparison between subjectivities for 20 different productivity facets.
©2021 Lamba and Madhusdhan - all rights reserved
Screenshot of evaluation result (©2020 Cadernos BAD, all rights reserved – reprinted under Creative Commons CC BY license, published in Lamba and Madhusudhan (2020))
The figure shows the evaluation results for library science ETDs in the PQDT Global database.